Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1187853
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dc.contributor.authorHODEL, L.
dc.contributor.authorWEGNER, J. D.
dc.contributor.authorGARNOT, V. S. F.
dc.contributor.authorGOMES, F. C. da R.
dc.contributor.authorVALENTIM, J. F.
dc.contributor.authorGARRETT, R. D.
dc.date.accessioned2026-06-25T21:24:47Z-
dc.date.available2026-06-25T21:24:47Z-
dc.date.created2026-06-25
dc.date.issued2026
dc.identifier.citationCommunications Sustainability, n. 1, article 98, 2026.
dc.identifier.issn3059-4308
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1187853-
dc.descriptionCattle ranching is a sustainability challenge worldwide, and in the Amazon, the planet’s largest tropical forest, it remains the main driver of deforestation. Yet, cattle numbers have typically been estimated from coarse census data or indirect proxies, limiting our ability to monitor land-use change at finer scales. Here, we introduce a novel approach that applies deep learning-based density estimation to very high-resolution satellite imagery to detect individual animals across the Brazilian Amazon.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectDeep learning
dc.subjectGanado
dc.subjectProducción de ganado bovino
dc.subjectDensidad poblacional
dc.subjectDensidad de pastoreo
dc.subjectUso de la tierra
dc.subjectTeledetección
dc.subjectBrazilian Amazon
dc.subjectAmazônia brasileira
dc.titleSpatial patterns of cattle densities across the Brazilian Amazon revealed by very high-resolution satellite imagery.
dc.typeArtigo de periódico
dc.subject.thesagroPecuária
dc.subject.thesagroBovinocultura
dc.subject.thesagroTaxa de Lotação
dc.subject.thesagroUso da Terra
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroSatélite
dc.subject.nalthesaurusLivestock
dc.subject.nalthesaurusCattle production
dc.subject.nalthesaurusPopulation density
dc.subject.nalthesaurusStocking rate
dc.subject.nalthesaurusLand use
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusSatellites
riaa.ainfo.id1187853
riaa.ainfo.lastupdate2026-06-25
dc.identifier.doihttps://doi.org/10.1038/s44458-026-00082-2
dc.contributor.institutionLEONIE HODEL, UNIVERSITY OF CAMBRIDGE; JAN D. WEGNER, UNIVERSITAT ZURICH; VIVIEN SAINTE FARE GARNOT, UNIVERSITAT ZURICH; FRANCISCO CARLOS DA ROCHA GOMES, CPAF-AC; JUDSON FERREIRA VALENTIM, CPAF-AC; RACHAEL D. GARRETT, UNIVERSITY OF CAMBRIDGE.
Appears in Collections:Artigo em periódico indexado (CPAF-AC)

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